Meanwhile, Artificial Neural Networks (ANNs) and deep learning have
shown excellent performance as tools applied in various fields such as
computer vision, speech recognition, and natural language processing,
and nonlinear predictive classifiers that can extract useful features
from input data [1]}, [2]}.
However, training ANNs typically requires optimizing a non-convex
objective function with multiple local optima and saddle
points [3]}, which is difficult and costly to
optimize. For this reason, neural networks with relatively simple
structures (i.e., single hidden layer) have gained renewed
attention [2]}. The architecture of these models is
easy to implement and allows reasonable performance through simple
optimization and fast learning processes. A promising technique for
the development of such networks is Randomization –
construction of a neural network with a subset of randomly generated
and fixed weights so that the remaining weights can be learned in a
straightforward linear optimization problem. By projecting inputs
randomly into a high-dimensional space and then focusing training
on finding weights that map this space to the output, learning can be
solved as a highly tractable linear least square
problem [1]}. Furthermore, not only
is the nature of the optimization problem simpler, but the number of
weights that need to be trained is only a fraction of the total
weights in the network. Such networks have universal approximation
properties [6]}, [7]}, [8]}, which has allows them to be broadly applied to
classification and regression problems.
